MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123138 A) filed by B V Raju Institute Of Technology, Narsapur, Telangana, on Dec. 6, for 'temporal deep learning models with hyperspectral data fusion for fine-grained crop physiological trait analysis.'

Inventor(s) include Sreekanth Kota; and Cheemaladinne Vengaiah.

The application for the patent was published on Jan. 2, under issue no. 01/2026.

According to the abstract released by the Intellectual Property India: "The invention presents a unified deep-learning framework that integrates hyperspectral imaging with advanced temporal modeling techniques to enable fine-grained analysis of crop physiological traits. Hyperspectral data provide rich spectral signatures that capture subtle biochemical and biophysical characteristics of plants, while temporal modeling reveals the progression of these traits across different growth stages. The proposed system combines spectral feature extraction modules with Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) networks, and Transformer-based encoders to capture long-range dependencies and dynamic physiological patterns. The framework processes sequential hyperspectral images collected over time, applying preprocessing steps such as band selection, normalization, and temporal alignment to enhance feature quality and reduce dimensionality. Fused spectral-temporal representations are used to detect early-stage stress indicators, nutrient deficiencies, pigment variations, disease onset, and other micro-level physiological changes that are often undetectable through conventional imaging or single-time-step analysis. Designed for scalability, the system can be deployed on field-based sensors, UAV platforms, or edge devices for real-time or near-real-time crop monitoring. Experimental evaluations demonstrate significant improvements in accuracy, robustness, and early detection capability compared to traditional hyperspectral or static deep learning methods. By providing timely and precise insights into crop health, the invention supports precision agriculture, improves resource management, and enhances overall crop productivity through proactive decision-making."

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